基于实时语义链表构建系统的改善定位研究
Research on Improved Localization Based on Real-Time Semantic Chain List Systems
- 电子学报 2025年53卷第5期 页码：1533-1540
- 作者机构：
1.武汉科技大学冶金装备及其控制教育部重点实验室，湖北武汉 430081
2.武汉科技大学机器人与智能系统研究院， 湖北武汉 430081
3.武汉科技大学机械传动与制造工程湖北省重点实验室，湖北武汉 430081
- 作者简介：
[ "蒋林 男，1976年12月出生于湖北省荆门市.博士，教授，博士研究生导师.主要研究方向为智能移动机器人环境探测、语义地图构建及定位导航研究. E-mail: jianglin76@wust.edu.cn" ]
[ "杨文琦 男，2000年12月出生于北京市.硕士研究生.主要研究方向为移动机器人语义建图及定位. E-mail: 1225392119@qq.com" ]
[ "雷斌 男，1979年12月出生于湖北省襄阳市.博士，副教授，硕士研究生导师.主要研究方向为智能机器人、群体机器人编队控制、定位导航、无线传感器网络和模式识别. E-mail: leibin@wust.edu.cn" ]
- 基金信息：国家重点研发计划(2019YFB1310000);国家自然科学基金(51874217)
DOI：10.12263/DZXB.20240336
中图分类号： TP242.6收稿：2024-04-25，
修回：2025-01-21，
纸质出版：2025-05-25
- 稿件说明：
移动端阅览
蒋林, 杨文琦, 雷斌, 等. 基于实时语义链表构建系统的改善定位研究[J]. 电子学报, 2025, 53(05): 1533-1540.
JIANG Lin, YANG Wen-qi, LEI Bin, et al. Research on Improved Localization Based on Real-Time Semantic Chain List Systems[J]. Acta Electronica Sinica, 2025, 53(05): 1533-1540.
蒋林, 杨文琦, 雷斌, 等. 基于实时语义链表构建系统的改善定位研究[J]. 电子学报, 2025, 53(05): 1533-1540. DOI：10.12263/DZXB.20240336
JIANG Lin, YANG Wen-qi, LEI Bin, et al. Research on Improved Localization Based on Real-Time Semantic Chain List Systems[J]. Acta Electronica Sinica, 2025, 53(05): 1533-1540. DOI：10.12263/DZXB.20240336
摘要
针对移动机器人领域自适应蒙特卡洛定位算法（Adaptive Monte Carlo Localization，AMCL）在相似及变化场景下易失效的问题，本文提出基于改进YOLOv8构建语义链表为AMCL提供预定位位姿的方法，改变粒子权重更新方式，进而提升定位准确性和鲁棒性.以YOLOv8为基础，结合信息聚集-分发机制和注意力尺度序列融合模块增强其Neck部分特征融合能力，并对模型进行剪枝，提升精度和速度；利用激光SLAM（Simultaneous Localization And Mapping）构建二维栅格地图，通过改进的YOLOv8提取物体语义并映射到地图上，得到二维语义地图，根据各连续语义物体之间的关系构建语义链表；在定位过程中，将机器人识别到的物体语义信息与语义链表进行匹配，为AMCL提供预定位位姿，改变其粒子更新方式进行精确定位，并基于词袋模型降低免疫障碍物遮挡导致的语义链断裂.在相似及变化场景下进行定位对比实验，实验结果验证了本文算法的有效性.
Abstract
To address the issue of AMCL (Adaptive Monte Carlo Localization) failure in similar and dynamic environments within the field of mobile robotics
this paper proposes a method based on the improved YOLOv8 to construct a semantic chain list
which provides a pre-localization pose for AMCL
altering the particle weight update mechanism to enhance localization accuracy and robustness. Built on the YOLOv8 architecture
the method integrates the gather-and-distribute mechanism and attentional scale sequence fusion module to enhance the feature fusion capabilities of the Neck section
while pruning the model to improve both accuracy and speed. Laser SLAM (Simultaneous Localization And Mapping) is used to construct a 2D grid map
and the improved YOLOv8 extracts object semantics and maps them onto the grid map
generating a 2D semantic map. A semantic chain list is constructed based on the relationships between consecutive semantic objects. During localization
the robot's detected object semantic information is matched with the semantic chain list to provide a pre-localization pose for AMCL
modifying the particle update mechanism for precise localization. Additionally
a bag-of-words model is employed to mitigate semantic chain breaks caused by occlusion from obstacles. Localization experiments in similar and dynamic environments validate the effectiveness of the proposed algorithm.
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references
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